def _get_ws(): global work_space if work_space is not None: return work_space sk = _get_sk() work_space = skil.WorkSpace(sk) return work_space
def test_model_creation_2(): sk = skil.Skil() work_space = skil.WorkSpace(sk) exp = skil.Experiment(work_space) model = skil.Model('keras_mnist.h5', experiment=exp) work_space.delete() exp.delete() model.delete()
def test_work_space_by_id(): global work_space global work_space_id sk = _get_sk() work_space = skil.WorkSpace(sk, name='test_ws') ws_id = work_space.id work_space_id = ws_id work_space2 = skil.get_workspace_by_id(sk, ws_id) assert work_space.name == work_space2.name
def test_transform_creation_2(): sk = skil.Skil() work_space = skil.WorkSpace(sk) exp = skil.Experiment(work_space) transform = skil.Transform('iris_tp.json', experiment=exp) transform.add_evaluation(0.42) work_space.delete() exp.delete() transform.delete()
def test_service_creation(): sk = skil.Skil() work_space = skil.WorkSpace(sk) exp = skil.Experiment(work_space) model = skil.Model('keras_mnist.h5', experiment=exp) model.add_evaluation(0.95) dep = skil.Deployment(sk) model.deploy(dep) work_space.delete() exp.delete() model.delete() dep.delete()
def test_work_space_creation(): global work_space work_space = skil.WorkSpace(sk)
import skil import numpy as np skil_server = skil.Skil() work_space = skil.WorkSpace(skil_server) experiment = skil.Experiment(work_space) transform = skil.Transform(transform='iris_tp.json', experiment=experiment) model = skil.Model(model='iris_model.h5', experiment=experiment) deployment = skil.Deployment(skil_server) pipeline = skil.Pipeline(deployment, model, transform) with open('iris.data', 'r') as f: data = np.array(f.readlines()) print(pipeline.predict(data))
import skil from keras.models import model_from_config import json # Load Keras model you want to train with open('keras_config.json', 'r') as f: model = model_from_config(json.load(f)) model.compile(loss='categorical_crossentropy', optimizer='sgd') # Create a SKIL model from it skil_server = skil.Skil() ws = skil.WorkSpace(skil_server) experiment = skil.Experiment(ws) model = skil.Model(model, model_id='keras_mnist_mlp_42', name='keras', experiment=experiment) # Register compute and storage resources. s3 = skil.resources.storage.S3( skil_server, 's3_resource', 'bucket_name', 'region') emr = skil.resources.compute.EMR( skil_server, 'emr_cluster', 'region', 'credential_uri', 'cluster_id') # Define your general training setup training_config = skil.jobs.TrainingJobConfiguration( skil_model=model, num_epochs=10, eval_type='ROC_MULTI_CLASS', storage_resource=s3, compute_resource=emr, data_set_provider_class='MnistProvider', eval_data_set_provider_class='MnistProvider', output_path='.') # Optionally specify a distributed training config.
def test_work_space_deletion(): sk = _get_sk() work_space = skil.WorkSpace(sk) work_space.delete()
def test_work_space_creation(): sk = skil.Skil() work_space = skil.WorkSpace(sk) work_space.delete()
def test_experiment_creation(): sk = skil.Skil() work_space = skil.WorkSpace(sk) exp = skil.Experiment(work_space) work_space.delete() exp.delete()